Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Leonel Rozo"'
Autor:
Monica Malvezzi, Arash Ajoudani, Virginia Ruiz Garate, Kensuke Harada, Maximo A. Roa, Maria Pozzi, Leonel Rozo
Publikováno v:
IEEE Robotics & Automation Magazine. 28:10-12
The articles in this special section aim to stimulate and gather publications describing how new approaches in the field of robotic manipulation can be (or have already been) transferred from research labs to the productive world. Novel robotic devel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fb4497578de2d50668ac9e3e3fd1da7d
https://doi.org/10.1109/mra.2021.3067685
https://doi.org/10.1109/mra.2021.3067685
Publikováno v:
The International Journal of Robotics Research. 38:833-852
Imitation learning has been studied widely as a convenient way to transfer human skills to robots. This learning approach is aimed at extracting relevant motion patterns from human demonstrations and subsequently applying these patterns to different
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::182c64953075a3bf09dc491f6a605379
https://doi.org/10.1177/0278364919846363
https://doi.org/10.1177/0278364919846363
Publikováno v:
The International Journal of Robotics Research
Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dfd342c8f0e79e92b422d8cabaaeca2a
https://pubmed.ncbi.nlm.nih.gov/33994629
https://pubmed.ncbi.nlm.nih.gov/33994629
Publikováno v:
Robotics: Science and Systems (R:SS)
Robotics: Science and Systems XVII
Beik-Mohammadi, H, Hauberg, S, Arvanitidis, G, Neumann, G & Rozo, L 2021, Learning Riemannian Manifolds for Geodesic Motion Skills . in Proceedings of Robotics: Science and Systems 2021 . Robotics: Science and System Xvii, Robotics: Science and Systems, 12/07/2021 . https://doi.org/10.15607/rss.2021.xvii.082
Robotics: Science and Systems
Robotics: Science and Systems XVII
Beik-Mohammadi, H, Hauberg, S, Arvanitidis, G, Neumann, G & Rozo, L 2021, Learning Riemannian Manifolds for Geodesic Motion Skills . in Proceedings of Robotics: Science and Systems 2021 . Robotics: Science and System Xvii, Robotics: Science and Systems, 12/07/2021 . https://doi.org/10.15607/rss.2021.xvii.082
Robotics: Science and Systems
*For robots to work alongside humans and perform in unstructured environments, they must learn new motion skills and adapt them to unseen situations on the fly. This demands learning models that capture relevant motion patterns, while offering enough
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3a1513613013a13bd7d288a4db993ae
Publikováno v:
IROS
Humans exhibit outstanding learning, planning and adaptation capabilities while performing different types of industrial tasks. Given some knowledge about the task requirements, humans are able to plan their limbs motion in anticipation of the execut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::143176602b2a05ea8498fd164292c518
https://doi.org/10.1109/iros45743.2020.9341353
https://doi.org/10.1109/iros45743.2020.9341353
Autor:
Nicolai Waniek, Markus Giftthaler, Leonel Rozo, Meng Guo, Marco Todescato, Matthias Ochs, Mathias Bürger, Patrick Kesper, Markus Spies, Andras Kupcsik, Philipp Schillinger
Publikováno v:
IROS
Enabling robots to quickly learn manipulation skills is an important, yet challenging problem. Such manipulation skills should be flexible, e.g., be able adapt to the current workspace configuration. Furthermore, to accomplish complex manipulation ta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8d5789e8a2484d9c6ba7cc3d95d47e03
http://arxiv.org/abs/2008.10471
http://arxiv.org/abs/2008.10471
Publikováno v:
Robotics and Autonomous Systems. 109:156-167
In order for robots to successfully carry out manipulation tasks, they require to exploit contact forces and variable impedance control. The conditions of such type of robotic tasks may significantly vary in dynamic environments, which demand robots
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45edb3e0a5d1e39f298914c4ed2110e7
https://doi.org/10.1016/j.robot.2018.07.008
https://doi.org/10.1016/j.robot.2018.07.008
Publikováno v:
IEEE Robotics and Automation Letters. 2:719-726
In this letter, we propose a novel method that enables the robot to autonomously devise an appropriate control strategy from human demonstrations without a prior knowledge of the demonstrated task. The method is primarily based on observing the patte
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e4e548e2874cdf20ecc121355fc23de
https://doi.org/10.1109/lra.2017.2651368
https://doi.org/10.1109/lra.2017.2651368
Publikováno v:
ICRA
Unstructured environments impose several challenges when robots are required to perform different tasks and adapt to unseen situations. In this context, a relevant problem arises: how can robots learn to perform various tasks and adapt to different c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee907eb24caffb1ac331f7453183a434
https://doi.org/10.1109/icra.2019.8794267
https://doi.org/10.1109/icra.2019.8794267
Publikováno v:
arXiv.org e-Print Archive
IROS
IROS
During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the literature. One of their most prominent features, in addition to extracting a mean trajectory from task demonstrations, is that they provide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5dbdde6374081b49c5cd76bcb5ae08e9